The Electronic Health Record (EHR) is a system for collecting and storing patient medical records as data that can be mechanically accessed, hence facilitating and assisting the medical decision-making process. EHRs exist in several formats, and each format lists thousands of keywords to classify patients data. The keywords are specific and are medical jargon; hence, data classification is very accurate. As the keywords constituting the formats of medical records express concepts by means of specific jargon without definitions or references, their proper use is left to clinicians and could be affected by their background, hence the interpretation of data could become slow or less accurate than that desired. This article presents an approach that accurately relates data in EHRs to ontologies in the medical realm. Thanks to ontologies, clinicians can be assisted when writing or analysing health records, e.g., our solution promptly suggests rigorous definitions for scientific terms, and automatically connects data spread over several parts of EHRs. The first step of our approach consists of converting selected data and keywords from several EHR formats into a format easier to parse, then the second step is merging the extracted data with specialised medical ontologies. Finally, enriched versions of the medical data are made available to professionals. The proposed approach was validated by taking samples of medical records and ontologies in the real world. The results have shown both versatility on handling data, precision of query results, and appropriate suggestions for relations among medical records.

Social network systems are constantly fed with text messages. While this enables rapid communication and global awareness, some messages could be aptly made to hurt or mislead. Automatically identifying meaningful parts of a sentence, such as, e.g., positive or negative sentiments in a phrase, would give valuable support for automatically flagging hateful messages, propaganda, etc. Many existing approaches concerned with the study of people’s opinions, attitudes and emotions and based on machine learning require an extensive labelled dataset and provide results that are not very decisive in many circumstances due to the complexity of the language structure and the fuzziness inherent in most of the techniques adopted. This paper proposes a deterministic approach that automatically identifies people’s sentiments at the sentence level. The approach is based on text analysis rules that are manually derived from the way Italian grammar works. Such rules are embedded in finite-state automata and then expressed in a way that facilitates checking unstructured Italian text. A few grammar rules suffice to analyse an ample amount of correctly formed text. We have developed a tool that has validated the proposed approach by analysing several hundreds of sentences gathered from social media: hence, they are actual comments given by users. Such a tool exploits parallel execution to make it ready to process many thousands of sentences in a fraction of a second. Our approach outperforms a well-known previous approach in terms of precision.

Drones have proven effective for acquiring aerial imagery, and when equipped with onboard analysis tools, they can automatically identify objects of interest. Neural-network methods for image analysis typically require large training datasets and substantial computational resources. By contrast, algorithmic techniques can detect objects using simple features, such as pixel colors, thereby reducing the need for extensive training and computational resources. Once trained, both types of system can analyze images in a short time. In our experiments, each approach has distinct strengths. The YOLO-based detector is more accurate for complex-shaped objects, such as trees, whereas the pixel-color approach performs better on sparser objects. This paper proposes YOLO-C3, a hybrid system designed for onboard drone image processing. By leveraging the strengths of both YOLO-based and pixel-based approaches, YOLO-C3 balances detection accuracy with estimation confidence. Trained on Mediterranean imagery dataset, the system is optimized for identifying natural objects, including citrus groves and trees. To assess the robustness of the image classifier, a K-fold cross-validation is performed. Compared to existing models, YOLO-C3 detects a wider range of natural objects with high accuracy and minimal latency, achieving a processing speed of 0.01 s per image. By performing object detection locally, drones can adapt their trajectories to support emergency response, helping to map safe corridors and locate buildings where people may be awaiting rescue after a natural disaster.

Social media networks provide an aggregation of news and content, allowing users to share and discuss topics of greatest interest to them. Users can enrich the news by providing context and opinions that are useful to other users. Understanding topics of interest sheds light on the collective thinking of a group of individuals and offers important insights for exploring a given field. Among the fields of interest on social media networks, finance stands out. Automatically identifying and organizing the main issues that users discuss can be useful for multiple purposes, e.g., identifying the preferred types of loans could be useful for refining targeted advertising. Our work aims to identify and organize the topics discussed on a social media network that are related to the financial sector. For this, we propose an approach that consists of analyzing posts from Reddit communities oriented to finance. First, posts were gathered and cleaned to remove punctuation, links, and images. Then, textual similarity was computed to match posts with classes from dedicated ontologies designed for the financial sector. Finally, the populated ontology was analyzed to identify clusters of concepts. The results showed that the proposed approach and corresponding tool can summarize topics from a large number of Reddit posts using the identified classes. Over 70% of posts were linked to ontologies when considering both posts and comments, which shows that the automatic support given to posts related to financial concepts had a high degree of success.

Large language models (LLMs) have shown remarkable results when tasked with the analysis and production of texts or images and for captioning images. Aerial images differ from other images since they exhibit many natural objects that have a highly variable color range and no clear contours. This paper reports to what extent an LLM, i.e., Llama-4, can be tasked with the identification and captioning in aerial images of natural objects, such as tree categories, uncultivated land, and some man-made objects, such as roads. This valuable automation is needed to scan large areas and detect the parts for which a sudden maintenance or an emergency intervention is due. Tests on the chosen LLM were performed against a custom image dataset built to overcome the limited availability of such a domain-specific aerial image set. To evaluate the identification and captioning results, the accuracy, precision and recall metrics were computed. The results given by a cutting-edge variant of Llama-4, namely Maverick, reveal its strengths and weaknesses in this context. Although it is remarkable that an out-of-the-box tool can give assistance in such a complex observation and detection task, substantial progress is needed for such a model to improve accuracy and constitute a reliable support, as accuracy is at most 58.6% and recall is at most 56.1%.